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SQLModel with developer ergonomics. Make SQLModel act like ActiveRecord.

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ActiveModel: ORM Wrapper for SQLModel

ActiveModel Logo

No, this isn't really ActiveModel. It's just a wrapper around SQLModel that provides a ActiveRecord-like interface.

SQLModel is not an ORM. It's a SQL query builder and a schema definition tool. This drives me nuts because the developer ergonomics are terrible because of this.

This package provides a thin wrapper around SQLModel that provides a more ActiveRecord-like interface with things like:

  • ActiveRecord-style Query & Persistence API: Fluent methods like save(), where(), find_or_create_by(), and upsert() for intuitive database operations.
  • Implicit Session Management: Automatically handles database sessions, eliminating boilerplate and making database interactions feel "magic".
  • Stripe-style IDs (TypeID): Native support for type-safe, prefixed, and sortable UUIDs with a built-in TypeIDMixin.
  • Timestamp Column Mixins: Standard created_at and updated_at tracking out of the box.
  • Lifecycle Hooks: Rails-style callbacks like before_save, after_create, and around_delete.
  • Automatic DB Comments: Syncs class and field-level docstrings directly to database table and column comments for better self-documentation.
  • Soft Deletion: Easily mark records as deleted with a deleted_at timestamp using the SoftDeletionMixin.
  • Smart Table & Constraint Naming: Consistent snake_case table names and standardized naming conventions for indexes and constraints.

[!TIP] This documentation is pretty bad. The tests and docstrs on code are the best way to learn how to use this.

Installation

uv add activemodel

Getting Started

First, setup your DB:

import activemodel
activemodel.init("sqlite:///database.db")

Create models:

from activemodel import BaseModel
from activemodel.mixins import TimestampsMixin, TypeIDMixin

class User(
    BaseModel,
    # optionally, obviously
    TimestampsMixin,
    # you can use a different pk type, but why would you?
    # put this mixin last otherwise `id` will not be the first column in the DB
    TypeIDMixin("user"),
    # wire this model into the DB, without this alembic will not generate a migration
    table=True
):
    a_field: str

You'll need to create the models in the DB. Alembic is the best way to do it, but you can cheat as well:

from sqlmodel import SQLModel

SQLModel.metadata.create_all(get_engine())

# now you can create a user! without managing sessions!
User(a_field="a").save()

Maybe you like JSON:

from sqlalchemy.dialects.postgresql import JSONB
from pydantic import BaseModel as PydanticBaseModel

from activemodel import BaseModel
from activemodel.mixins import PydanticJSONMixin, TypeIDMixin, TimestampsMixin

class SubObject(PydanticBaseModel):
    name: str
    value: int

class User(
    BaseModel,
    TimestampsMixin,
    PydanticJSONMixin,
    TypeIDMixin("user"),
    table=True
):
    list_field: list[SubObject] = Field(sa_type=JSONB)

You'll probably want to query the model. Look ma, no sessions!

User.where(id="user_123").all()

# or, even better, for this case
User.one("user_123")

Magically creating sessions for DB operations is one of the main problems this project tackles. Even better, you can set a single session object to be used for all DB operations. This is helpful for DB transactions, specifically rolling back DB operations on each test.

Usage

Lifecycle Hooks

BaseModel supports a small Rails-style lifecycle hook system.

The implemented hooks today are:

  • Create/update: before_create, after_create, before_update, after_update, before_save, after_save, around_save
  • Delete: before_delete, after_delete, around_delete
  • Read: after_find, after_initialize

Hook methods are optional. If a method with one of those names exists on the model, ActiveModel will call it at the appropriate time.

from contextlib import contextmanager

from activemodel import BaseModel


class User(BaseModel, table=True):
    id: int | None = Field(default=None, primary_key=True)
    email: str

    def before_save(self):
        self.email = self.email.strip().lower()

    def after_find(self):
        print(f"loaded user {self.id}")

    def after_initialize(self):
        print(f"initialized user {self.id}")

    @contextmanager
    def around_save(self):
        print("before save")
        yield
        print("after save")

Some important semantics:

  • after_initialize runs on plain construction, so User(email="a@example.com") will trigger it even before the record is saved.
  • Database-backed finder/query loads run after_find and then after_initialize.
  • after_find is not called for plain construction.
  • find_or_initialize_by() follows the Rails-style split: the existing-record path runs after_find then after_initialize, while the new-instance path only runs after_initialize.
  • around_save and around_delete must be context managers.

Current ordering is:

  • Create: before_create -> before_save -> around_save -> after_create -> after_save
  • Update: before_update -> before_save -> around_save -> after_update -> after_save
  • Delete: before_delete -> around_delete -> after_delete
  • DB load: after_find -> after_initialize
  • Plain construction: after_initialize

There is one important scope limit to know about:

  • refresh() does not currently replay Rails-style read callbacks. It refreshes the object from the database, but it does not currently trigger after_find / after_initialize the way Rails reload effectively does.

Also note that after_find / after_initialize only run for model instances. Lower-level query paths that return None, counts, scalars, or raw SQLAlchemy result objects are outside that contract.

Pytest

TODO detail out truncation and transactions

Integrating Alembic

Detailed instructions on how to integrate Alembic into your project can be found in the Alembic Integration documentation.

Query Wrapper

This tool is added to all BaseModels and makes it easy to write SQL queries. Some examples:

Easy Database Sessions

I hate the idea f

  • Behavior should be intuitive and easy to understand. If you run save(), it should save, not stick the save in a transaction.
  • Don't worry about dead sessions. This makes it easy to lazy-load computed properties and largely eliminates the need to think about database sessions.

There are a couple of thorny problems we need to solve for here:

  • In-memory fastapi servers are not the same as a uvicorn server, which is threaded and uses some sort of threadpool model for handling async requests. I don't claim to understand the entire implementation. For global DB session state (a) we can't use global variables (b) we can't use thread-local variables.iset

https://github.com/tomwojcik/starlette-context

Example SQLAlchemy Queries

  • Conditional: Scrape.select().where(Scrape.id < last_scraped.id).all()
  • Equality: MenuItem.select().where(MenuItem.menu_id == menu.id).all()
  • IN example: CanonicalMenuItem.select().where(col(CanonicalMenuItem.id).in_(canonized_ids)).all()
  • Compound where query: User.where((User.last_active_at != None) & (User.last_active_at > last_24_hours)).count()
  • How to select a field in a JSONB column: str(HostScreeningOrder.form_data["email"].as_string())
  • JSONB where clause: Screening.where(Screening.theater_location['name'].astext.ilike('%AMC%'))

SQLModel Internals

SQLModel & SQLAlchemy are tricky. Here are some useful internal tricks:

  • __sqlmodel_relationships__ is where any RelationshipInfo objects are stored. This is used to generate relationship fields on the object.
  • inspect(type(self)).relationships['distribution'] to inspect a specific generated relationship object
  • ModelClass.relationship_name.property.local_columns
  • Get cached fields from a model object_state(instance).dict.get(field_name)
  • Set the value on a field, without marking it as dirty attributes.set_committed_value(instance, field_name, val)
  • Is a model dirty instance_state(instance).modified
  • select(Table).outerjoin?? won't work in a ipython session, but Table.__table__.outerjoin?? will. __table__ is a reference to the underlying SQLAlchemy table record.
  • get_engine().pool.stats() is helpful for inspecting connection pools and limits\

TypeID

I'm a massive fan of Stripe-style prefixed UUIDs. There's an excellent project that defined a clear spec for these IDs. I've used the python implementation of this spec and developed a clean integration with SQLModel that plays well with fastapi as well.

Here's an example of defining a relationship:

import uuid

from activemodel import BaseModel
from activemodel.mixins import TimestampsMixin, TypeIDMixin
from activemodel.types import TypeIDType
from sqlmodel import Field, Relationship

from .patient import Patient

class Appointment(
    BaseModel,
    # this adds an `id` field to the model with the correct type
    TypeIDMixin("appointment"),
    table=True
):
    # `foreign_key` is a activemodel method to generate the right `Field` for the relationship
    # TypeIDType is really important here for fastapi serialization
    doctor_id: TypeIDType = Doctor.foreign_key()
    doctor: Doctor = Relationship()

Here's how to get the prefix associated with a given field:

model_class.__model__.model_fields["field_name"].sa_column.type.prefix

Limitations

Validation

SQLModel does not currently support pydantic validations (when table=True). This is very surprising, but is actually the intended functionality:

For validation:

  • When consuming API data, use a separate shadow model to validate the data with table=False and then inherit from that model in a model with table=True.
  • When validating ORM data, use SQL Alchemy hooks.

Development

Watch out for subtle differences across pydantic versions. There's some sneaky type inspection stuff in PydanticJSONMixin that will break in subtle ways if the python, pydantic, etc versions don't match.

import pydantic
print(pydantic.VERSION)
import sys
print(sys.version)

Related Projects

Inspiration

Upstream Changes


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